mirror of
https://github.com/wassname/Open-Assistant.git
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Andreas Köpf
14fa08e2e7
* add query_incomplete_rankings() * Add SQL queries for TreeManager task selection * first working version of TreeManager.next_task() * remove old generate_task(), add mandatory_labels to text_labels task * Add ConversationMessage list to Ranking tasks * add more sophisticated sql queries to find extendible trees * add TreeManager.query_extendible_parents() * fix task validation, seed data insertion (reviewed) * provide user for task selection in text-frontend * enter 'growing' state * enter 'aborted_low_grade' state * enter 'ranking' state * check tree 'growing' state upon relpy insertion * exclude user from labeling their own messages (added DEBUG_ALLOW_SELF_LABELING setting) * add DEBUG_ALLOW_SELF_LABELING to docker-compose.yaml * fix ranking submission * add query_tree_ranking_results() * add ranked_message_ids to RankingReactionPayload * fix reply_messages instead of prompt_messages * incorment 'ranking_count' of ranked replies * added logic to check_condition_for_scoring_state * changes to msg_tree_state_machine * pre-commit changes * enter 'ready_for_scoring' state * re-add HF embedding call (lost during merge) * use prepare_conversation() helper for seed-data creation * Partially add user specified task selection Co-authored-by: Daniel Hug <danielpatrickhug@gmail.com>
66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
from enum import Enum
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from typing import Any, Dict
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import aiohttp
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from loguru import logger
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from oasst_backend.config import settings
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from oasst_shared.exceptions import OasstError, OasstErrorCode
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class HfUrl(str, Enum):
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HUGGINGFACE_TOXIC_ROBERTA = ("https://api-inference.huggingface.co/models/unitary/multilingual-toxic-xlm-roberta",)
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HUGGINGFACE_FEATURE_EXTRACTION = "https://api-inference.huggingface.co/pipeline/feature-extraction"
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class HfEmbeddingModel(str, Enum):
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MINILM = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
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class HuggingFaceAPI:
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"""Class Object to make post calls to endpoints for inference in models hosted in HuggingFace"""
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def __init__(
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self,
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api_url: str,
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):
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# The API endpoint we want to access
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self.api_url: str = api_url
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# Access token for the api
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self.api_key: str = settings.HUGGING_FACE_API_KEY
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# Headers going to be used
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self.headers: Dict[str, str] = {"Authorization": f"Bearer {self.api_key}"}
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async def post(self, input: str) -> Any:
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"""Post request to the endpoint to get an inference
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Args:
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input (str): the input that we will pass to the model
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Raises:
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OasstError: in the case we get a bad response
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Returns:
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inference: the inference we obtain from the model in HF
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"""
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async with aiohttp.ClientSession() as session:
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payload: Dict[str, str] = {"inputs": input}
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async with session.post(self.api_url, headers=self.headers, json=payload) as response:
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# If we get a bad response
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if not response.ok:
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logger.error(response)
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logger.info(self.headers)
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raise OasstError(
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f"Response Error HuggingFace API (Status: {response.status})",
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error_code=OasstErrorCode.HUGGINGFACE_API_ERROR,
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)
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# Get the response from the API call
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inference = await response.json()
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return inference
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